Machine vision has many commercial applications and therefore, has attracted much attention in recent years. Accordingly many machine vision techniques and methods have been developed for detecting various image patterns and objects including deformable objects such as human faces.
The ability to recognize an image pattern or object such as a human face is an important machine vision problem. Present face recognition methods used in person identification systems, typically employ a face recognizer which determines the identity of a human face image. In order to identify the image the system compares the face to other faces stored in a database in order to identify the person. Systems used in many visual monitoring and surveillance applications, accomplish person identification by a process whereby the system determines the position of the human eyes from an image or an image sequence containing the human face. Once the position of the eyes is determined, all of other important facial features, such as the positions of the nose and the mouth, are determined by methods that use correlation templates, (2) spatial image invariants, (3) view-based eigen-spaces, etc. The use of one of these methods enables the system to recognize a face from a given face database, and can also be used to perform a variety of other related tasks.
A key issue and difficulty in face recognition is to account for the wide range of illumination conditions which can exist in an image. Existing methods find this task difficult to impossible when an illumination change, such as a shadow, is in the image of interest. This is usually due to the system's inability to distinguish between the background light and the foreground object. Face recognition in commercial outdoor applications such as ATM access control, surveillance, video conferencing, and the like, typically involves images with varying light conditions. Such applications pose one of many major challenges for conventional machine vision techniques which were primarily developed for applications involving controlled lighting conditions.
The prior art has proposed many image enhancement techniques and methods. A popular one of these methods removes the unwanted shadow effect in an image by subtracting off a low-passed version of the image with a large kernel size. The operates to remove the slowly varying lighting condition which is often associated with the shadow or back-ground lighting. However, it is often not possible to accurately model a lighting change in an image as a simple additive effect. In practice, prior art image enhancement methods amplify the range of dark to bright in an image, which is similar to the effect of increasing the gain of a camera.
It is, therefore, an object of the present invention to provide an improved image enhancement method that can be used as a preprocessing step to subsequent vision processing techniques, which generates an image that is substantially invariant to lighting changes.